The number of neighbors collected k: The influence of the number of neighbors on the effect of the algorithm is a hot research issue [1]}, [2]}, [3]}. In this section, we study the influence of the number of neighbors on the recommendation based on the KG by changing the number of neighbors collected k. The results are shown in the table REF . The experimental results clearly show that as the number of neighbors k increases, the AUC value also increases. This is because the small \(k\)  does not have enough capacity to merge neighborhood information. However, it is observed that the AUC value in the job data set becomes smaller. This may be similar to the reason for the dimension \(d\) , that is, the job data set is too sparse, and increasing the value of k cannot obtain enough domain information, but as k increases, it brings noise .
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